Searching big datasets can be computationally intensive and time consuming. Specifically, search algorithms can be described in terms of Big O notation. Big O notation describes the limiting behavior of a function when the function's arguments extend towards a particular value (e.g., infinity). Accordingly, Big O notation can be employed to classify algorithms based on changes to run time as the size of the input dataset grows. Unfortunately, even very efficient search algorithms can require significant run times over a big dataset as such algorithms scale according to the size of the dataset. To make query answering feasible in big datasets, mechanisms may be employed to allow algorithms to scale independently of the size of the dataset. This concept is known as scale independence.